Systems and methods for determining individual and group risk scores

ABSTRACT

Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user&#39;s level of exposure to email.

RELATED APPLICATIONS

This patent application claims the benefit of and priority to U.S.Provisional Patent Application No. 62/672,386, titled “SYSTEMS ANDMETHODS FOR CALCULATING METRICS IN A SECURITY AWARENESS SYSTEM,” andfiled May 16, 2018, and also claims the benefit of and priority to U.S.Provisional Patent Application No. 62/672,390, titled “SYSTEMS ANDMETHODS FOR DETERMINING INDIVIDUAL AND GROUP RISK SCORES,” and filed May16, 2018, the contents of all of which are hereby incorporated herein byreference in its entirety for all purposes

FIELD OF THE DISCLOSURE

This disclosure generally relates to determination of vulnerabilityscores for malicious cyberattacks using artificial intelligence, and tosystems and methods for calculating metrics in a security awarenesssystem while ensuring that the results of simulated phishing attacks areattributed to the correct user.

BACKGROUND OF THE DISCLOSURE

It can be useful to perform simulated phishing attacks on an individualor set of individuals for the purposes of extracting information from adevice used by the individuals. A phishing attack involves an attempt toacquire sensitive information such as usernames, passwords, credit carddetails, etc., often for malicious reasons, possible by masquerading asa trustworthy entity. For example, an email may be sent to a target, theemail having an attachment that performs malicious actions when executedor a link to a webpage that either performs malicious actions whenaccessed or prompts the user to execute a malicious program. Maliciousactions may include malicious data collection or actions harmful to thenormal functioning of a device on which the email was activated, or anyother malicious actions capable of being performed by a program or a setof programs.

A method of performing simulated phishing attacks is as follows. Atarget is defined as the user for whom the simulated phishing attack isdirected, i.e. the user that is being tested. A simulated phishingmessage is sent to the target's address. The message can masquerade as amessage from a party known to the target, such as an executive of thecompany that employs the target. In some embodiments, the message canappear to be sent from a party unknown to the target. The message may bedesigned to appear interesting to the target and may make an offer orpromise e.g. access to an interesting tidbit of news, access to usefulcomputer software, access to knowledge of how to perform a money-makingscheme, or any other thing that may be of interest. In someimplementations, the message may request that the target perform acertain action, such as providing sensitive information by replying tothe message or transferring money to an account owned by the attackerand then sending a reply message to confirm that the money has beentransferred. The message may request the target to perform any actionthat could result in a security breach if the simulated phishing messagewas a real phishing message.

A simulated phishing attack may test the readiness of a security systemor users of a system to handle phishing attacks such that maliciousactions are prevented. A simulated phishing attack may, for example,target a large number of users, such as employees of an organization.Such an attack may be performed by a party friendly or neutral to thetargets of the simulated attack. In one type of simulated phishingattack, an attempt is made to extract sensitive information usingphishing methods, and any extracted information is used not formalicious purposes, but as part of a process of detecting weaknesses insecurity. Performing a simulated phishing attack can help exposeweaknesses in the security infrastructure meant to protect users and/ordevices from phishing attacks or other computerized, cyber, or digitalattacks. It may also expose a lack of vigilance and/or know-how in auser or set of users of a device in minimizing risk associated with suchattacks. This can allow a security manager to pinpoint specific issuesto be resolved and to bolster security as appropriate. A simulatedphishing attack may be performed by e.g. a security manager, or by athird party on behalf of a security manager.

BRIEF SUMMARY OF THE DISCLOSURE

A server, for example a security awareness server or an artificialintelligence machine learning system may establish a risk score orvulnerable for a user of a security awareness system, or for a group ofusers of a security awareness system. The server may create a frequencyscore for a user, which predicts the frequency at which the user is tobe hit with a malicious attack. The frequency score may be based on atleast a job score, which may be represented by a value that is based onthe type of job the user has, and a breach score that may be representedby a value that is based on the user's level of exposure to email.

The server may also determine a propensity score that identifies thepropensity of the user to respond to the hit of the malicious attack. Apredictive model with an input of the user's history of whether or notthe user responded with a type of response for a given hit of themalicious attack may be trained and the propensity score may be based onthe training of this model.

The server may also determine a severity score that identifies howsevere the outcome is of the user's response to the hit of the maliciousattack. The severity score may be based on the job score of the user,and may also be based on how much access the user has, for example tocritical systems and servers of their organization.

Based on the frequency score, the propensity score, and the severityscore, the server may establish a risk score for the user. The riskscore model may be a function of these three scores, and the functionmay be a weighted function or logarithmic function of these threescores. A group score can be calculated based on a function of riskscores of each user within the group of users.

Based on at least the risk score, the server may display a probabilitythat the user will respond to a subsequent hit of a type of maliciousattack at a point in time. Information contained in a security awarenesssystem may be combined with information from external sources and usedcollectively to profile a user or group of users' past behavior. Thisprofile may be then used to predict a user's future behavior. Where auser's overall vulnerability can be characterized, a system maypro-actively act to protect the user, the system, or the organizationfrom actions that the user may make in the future. Accordingly, newsystems and methods for determine vulnerability scores are needed.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects, features, and advantages ofthe disclosure will become more apparent and better understood byreferring to the following description taken in conjunction with theaccompanying drawings, in which:

FIG. 1A is a block diagram depicting an embodiment of a networkenvironment comprising client device in communication with serverdevice;

FIG. 1B is a block diagram depicting a could computing environmentcomprising client device in communication with cloud service providers;

FIGS. 1C and 1D are block diagrams depicting embodiments of computingdevices useful in connection with the methods and systems describedherein;

FIG. 2 illustrates some of the architecture of an implementation of asystem capable of determining vulnerability scores for maliciouscyberattacks using artificial intelligence as part of a securityawareness system;

FIG. 3 depicts one embodiment of a method for calculating risk scores;

FIG. 4 illustrates the predictive performance of the model to calculaterisk scores.

DETAILED DESCRIPTION

For purposes of reading the description of the various embodimentsbelow, the following descriptions of the sections of the specificationsand their respective contents may be helpful:

Section A describes a network environment and computing environmentwhich may be useful for practicing embodiments described herein.

Section B describes an artificial intelligence network and environmentwhich may be useful for practicing embodiments described herein.

Section C describes embodiments of systems and methods determiningvulnerability scores for malicious cyberattacks, for example usingartificial intelligence as part of a security awareness system.

A. Computing and Network Environment

Prior to discussing specific embodiments of the present solution, it maybe helpful to describe aspects of the operating environment as well asassociated system components (e.g., hardware elements) in connectionwith the methods and systems described herein. Referring to FIG. 1A, anembodiment of a network environment is depicted. In brief overview, thenetwork environment includes one or more clients 102 a-102 n (alsogenerally referred to as local machine(s) 102, client(s) 102, clientnode(s) 102, client machine(s) 102, client computer(s) 102, clientdevice(s) 102, endpoint(s) 102, or endpoint node(s) 102) incommunication with one or more servers 106 a-106 n (also generallyreferred to as server(s) 106, node 106, or remote machine(s) 106) viaone or more networks 104. In some embodiments, a client 102 has thecapacity to function as both a client node seeking access to resourcesprovided by a server and as a server providing access to hostedresources for other clients 102 a-102 n.

Although FIG. 1A shows a network 104 between the clients 102 and theservers 106, the clients 102 and the servers 106 may be on the samenetwork 104. In some embodiments, there are multiple networks 104between the clients 102 and the servers 106. In one of theseembodiments, a network 104′ (not shown) may be a private network and anetwork 104 may be a public network. In another of these embodiments, anetwork 104 may be a private network and a network 104′ a publicnetwork. In still another of these embodiments, networks 104 and 104′may both be private networks.

The network 104 may be connected via wired or wireless links. Wiredlinks may include Digital Subscriber Line (DSL), coaxial cable lines, oroptical fiber lines. The wireless links may include BLUETOOTH, Wi-Fi,Worldwide Interoperability for Microwave Access (WiMAX), an infraredchannel or satellite band. The wireless links may also include anycellular network standards used to communicate among mobile devices,including standards that qualify as 1G, 2G, 3G, or 4G. The networkstandards may qualify as one or more generation of mobiletelecommunication standards by fulfilling a specification or standardssuch as the specifications maintained by International TelecommunicationUnion. The 3G standards, for example, may correspond to theInternational Mobile Telecommunications-2000 (IMT-2000) specification,and the 4G standards may correspond to the International MobileTelecommunications Advanced (IMT-Advanced) specification. Examples ofcellular network standards include AMPS, GSM, GPRS, UMTS, LTE, LTEAdvanced, Mobile WiMAX, and WiMAX-Advanced. Cellular network standardsmay use various channel access methods e.g. FDMA, TDMA, CDMA, or SDMA.In some embodiments, different types of data may be transmitted viadifferent links and standards. In other embodiments, the same types ofdata may be transmitted via different links and standards.

The network 104 may be any type and/or form of network. The geographicalscope of the network 104 may vary widely and the network 104 can be abody area network (BAN), a personal area network (PAN), a local-areanetwork (LAN), e.g. Intranet, a metropolitan area network (MAN), a widearea network (WAN), or the Internet. The topology of the network 104 maybe of any form and may include, e.g., any of the following:point-to-point, bus, star, ring, mesh, or tree. The network 104 may bean overlay network which is virtual and sits on top of one or morelayers of other networks 104′. The network 104 may be of any suchnetwork topology as known to those ordinarily skilled in the art capableof supporting the operations described herein. The network 104 mayutilize different techniques and layers or stacks of protocols,including, e.g., the Ethernet protocol, the internet protocol suite(TCP/IP), the ATM (Asynchronous Transfer Mode) technique, the SONET(Synchronous Optical Networking) protocol, or the SDH (SynchronousDigital Hierarchy) protocol. The TCP/IP internet protocol suite mayinclude application layer, transport layer, internet layer (including,e.g., IPv6), or the link layer. The network 104 may be a type of abroadcast network, a telecommunications network, a data communicationnetwork, or a computer network.

In some embodiments, the system may include multiple, logically-groupedservers 106. In one of these embodiments, the logical group of serversmay be referred to as a server farm 38 or a machine farm 38. In anotherof these embodiments, the servers 106 may be geographically dispersed.In other embodiments, a machine farm 38 may be administered as a singleentity. In still other embodiments, the machine farm 38 includes aplurality of machine farms 38. The servers 106 within each machine farm38 can be heterogeneous—one or more of the servers 106 or machines 106can operate according to one type of operating system platform (e.g.,WINDOWS NT, manufactured by Microsoft Corp. of Redmond, Wash.), whileone or more of the other servers 106 can operate on according to anothertype of operating system platform (e.g., Unix, Linux, or Mac OS X).

In one embodiment, servers 106 in the machine farm 38 may be stored inhigh-density rack systems, along with associated storage systems, andlocated in an enterprise data center. In this embodiment, consolidatingthe servers 106 in this way may improve system manageability, datasecurity, the physical security of the system, and system performance bylocating servers 106 and high-performance storage systems on localizedhigh-performance networks. Centralizing the servers 106 and storagesystems and coupling them with advanced system management tools allowsmore efficient use of server resources.

The servers 106 of each machine farm 38 do not need to be physicallyproximate to another server 106 in the same machine farm 38. Thus, thegroup of servers 106 logically grouped as a machine farm 38 may beinterconnected using a wide-area network (WAN) connection or ametropolitan-area network (MAN) connection. For example, a machine farm38 may include servers 106 physically located in different continents ordifferent regions of a continent, country, state, city, campus, or room.Data transmission speeds between servers 106 in the machine farm 38 canbe increased if the servers 106 are connected using a local-area network(LAN) connection or some form of direct connection. Additionally, aheterogeneous machine farm 38 may include one or more servers 106operating according to a type of operating system, while one or moreother servers 106 execute one or more types of hypervisors rather thanoperating systems. In these embodiments, hypervisors may be used toemulate virtual hardware, partition physical hardware, virtualizephysical hardware, and execute virtual machines that provide access tocomputing environments, allowing multiple operating systems to runconcurrently on a host computer. Native hypervisors may run directly onthe host computer. Hypervisors may include VMware ESX/ESXi, manufacturedby VMWare, Inc., of Palo Alto, Calif.; the Xen hypervisor, an opensource product whose development is overseen by Citrix Systems, Inc.;the HYPER-V hypervisors provided by Microsoft or others. Hostedhypervisors may run within an operating system on a second softwarelevel. Examples of hosted hypervisors may include VMware Workstation andVIRTUALBOX.

Management of the machine farm 38 may be de-centralized. For example,one or more servers 106 may comprise components, subsystems and modulesto support one or more management services for the machine farm 38. Inone of these embodiments, one or more servers 106 provide functionalityfor management of dynamic data, including techniques for handlingfailover, data replication, and increasing the robustness of the machinefarm 38. Each server 106 may communicate with a persistent store and, insome embodiments, with a dynamic store.

Server 106 may be a file server, application server, web server, proxyserver, appliance, network appliance, gateway, gateway server,virtualization server, deployment server, SSL VPN server, or firewall.In one embodiment, the server 106 may be referred to as a remote machineor a node. In another embodiment, a plurality of nodes 290 may be in thepath between any two communicating servers.

Referring to FIG. 1B, a cloud computing environment is depicted. A cloudcomputing environment may provide client 102 with one or more resourcesprovided by a network environment. The cloud computing environment mayinclude one or more clients 102 a-102 n, in communication with the cloud108 over one or more networks 104. Clients 102 may include, e.g., thickclients, thin clients, and zero clients. A thick client may provide atleast some functionality even when disconnected from the cloud 108 orservers 106. A thin client or a zero client may depend on the connectionto the cloud 108 or server 106 to provide functionality. A zero clientmay depend on the cloud 108 or other networks 104 or servers 106 toretrieve operating system data for the client device. The cloud 108 mayinclude back end platforms, e.g., servers 106, storage, server farms ordata centers.

The cloud 108 may be public, private, or hybrid. Public clouds mayinclude public servers 106 that are maintained by third parties to theclients 102 or the owners of the clients. The servers 106 may be locatedoff-site in remote geographical locations as disclosed above orotherwise. Public clouds may be connected to the servers 106 over apublic network. Private clouds may include private servers 106 that arephysically maintained by clients 102 or owners of clients. Privateclouds may be connected to the servers 106 over a private network 104.Hybrid clouds 108 may include both the private and public networks 104and servers 106.

The cloud 108 may also include a cloud-based delivery, e.g. Software asa Service (SaaS) 110, Platform as a Service (PaaS) 112, andInfrastructure as a Service (IaaS) 114. IaaS may refer to a user rentingthe use of infrastructure resources that are needed during a specifiedtime period. IaaS providers may offer storage, networking, servers orvirtualization resources from large pools, allowing the users to quicklyscale up by accessing more resources as needed. Examples of IaaS includeAMAZON WEB SERVICES provided by Amazon.com, Inc., of Seattle, Wash.,RACKSPACE CLOUD provided by Rackspace US, Inc., of San Antonio, Tex.,Google Compute Engine provided by Google Inc. of Mountain View, Calif.,or RIGHTSCALE provided by RightScale, Inc., of Santa Barbara, Calif.PaaS providers may offer functionality provided by IaaS, including,e.g., storage, networking, servers or virtualization, as well asadditional resources such as, e.g., the operating system, middleware, orruntime resources. Examples of PaaS include WINDOWS AZURE provided byMicrosoft Corporation of Redmond, Wash., Google App Engine provided byGoogle Inc., and HEROKU provided by Heroku, Inc. of San Francisco,Calif. SaaS providers may offer the resources that PaaS provides,including storage, networking, servers, virtualization, operatingsystem, middleware, or runtime resources. In some embodiments, SaaSproviders may offer additional resources including, e.g., data andapplication resources. Examples of SaaS include GOOGLE APPS provided byGoogle Inc., SALESFORCE provided by Salesforce.com Inc. of SanFrancisco, Calif., or OFFICE 365 provided by Microsoft Corporation.Examples of SaaS may also include data storage providers, e.g. DROPBOXprovided by Dropbox, Inc. of San Francisco, Calif., Microsoft SKYDRIVEprovided by Microsoft Corporation, Google Drive provided by Google Inc.,or Apple ICLOUD provided by Apple Inc. of Cupertino, Calif.

Clients 102 may access IaaS resources with one or more IaaS standards,including, e.g., Amazon Elastic Compute Cloud (EC2), Open CloudComputing Interface (OCCI), Cloud Infrastructure Management Interface(CIMI), or OpenStack standards. Some IaaS standards may allow clientsaccess to resources over HTTP and may use Representational StateTransfer (REST) protocol or Simple Object Access Protocol (SOAP).Clients 102 may access PaaS resources with different PaaS interfaces.Some PaaS interfaces use HTTP packages, standard Java APIs, JavaMailAPI, Java Data Objects (JDO), Java Persistence API (JPA), Python APIs,web integration APIs for different programming languages including,e.g., Rack for Ruby, WSGI for Python, or PSGI for Perl, or other APIsthat may be built on REST, HTTP, XML, or other protocols. Clients 102may access SaaS resources through the use of web-based user interfaces,provided by a web browser (e.g. GOOGLE CHROME, Microsoft INTERNETEXPLORER, or Mozilla Firefox provided by Mozilla Foundation of MountainView, Calif.). Clients 102 may also access SaaS resources throughsmartphone or tablet applications, including, e.g., Salesforce SalesCloud, or Google Drive app. Clients 102 may also access SaaS resourcesthrough the client operating system, including, e.g., Windows filesystem for DROPBOX.

In some embodiments, access to IaaS, PaaS, or SaaS resources may beauthenticated. For example, a server or authentication server mayauthenticate a user via security certificates, HTTPS, or API keys. APIkeys may include various encryption standards such as, e.g., AdvancedEncryption Standard (AES). Data resources may be sent over TransportLayer Security (TLS) or Secure Sockets Layer (SSL).

The client 102 and server 106 may be deployed as and/or executed on anytype and form of computing device, e.g. a computer, network device orappliance capable of communicating on any type and form of network andperforming the operations described herein. FIGS. 1C and 1D depict blockdiagrams of a computing device 100 useful for practicing an embodimentof the client 102 or a server 106. As shown in FIGS. 1C and 1D, eachcomputing device 100 includes a central processing unit 121, and a mainmemory unit 122. As shown in FIG. 1C, a computing device 100 may includea storage device 128, an installation device 116, a network interface118, an I/O controller 123, display devices 124 a-124 n, a keyboard 126and a pointing device 127, e.g. a mouse. The storage device 128 mayinclude, without limitation, an operating system, software, and asoftware of a simulated phishing attack system 120. As shown in FIG. 1D,each computing device 100 may also include additional optional elements,e.g. a memory port 103, a bridge 170, one or more input/output devices130 a-130 n (generally referred to using reference numeral 130), and acache memory 140 in communication with the central processing unit 121.

The central processing unit 121 is any logic circuitry that responds toand processes instructions fetched from the main memory unit 122. Inmany embodiments, the central processing unit 121 is provided by amicroprocessor unit, e.g.: those manufactured by Intel Corporation ofMountain View, Calif.; those manufactured by Motorola Corporation ofSchaumburg, Ill.; the ARM processor and TEGRA system on a chip (SoC)manufactured by Nvidia of Santa Clara, Calif.; the POWER7 processor,those manufactured by International Business Machines of White Plains,N.Y.; or those manufactured by Advanced Micro Devices of Sunnyvale,Calif. The computing device 100 may be based on any of these processors,or any other processor capable of operating as described herein. Thecentral processing unit 121 may utilize instruction level parallelism,thread level parallelism, different levels of cache, and multi-coreprocessors. A multi-core processor may include two or more processingunits on a single computing component. Examples of a multi-coreprocessors include the AMD PHENOM IIX2, INTEL CORE i5 and INTEL CORE i7.

Main memory unit 122 may include one or more memory chips capable ofstoring data and allowing any storage location to be directly accessedby the microprocessor 121. Main memory unit 122 may be volatile andfaster than storage 128 memory. Main memory units 122 may be Dynamicrandom-access memory (DRAM) or any variants, including static randomaccess memory (SRAM), Burst SRAM or SynchBurst SRAM (BSRAM), Fast PageMode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended Data Output RAM(EDO RAM), Extended Data Output DRAM (EDO DRAM), Burst Extended DataOutput DRAM (BEDO DRAM), Single Data Rate Synchronous DRAM (SDR SDRAM),Double Data Rate SDRAM (DDR SDRAM), Direct Rambus DRAM (DRDRAM), orExtreme Data Rate DRAM (XDR DRAM). In some embodiments, the main memory122 or the storage 128 may be non-volatile; e.g., non-volatile readaccess memory (NVRAM), flash memory non-volatile static RAM (nvSRAM),Ferroelectric RAM (FeRAM), Magnetoresistive RAM (MRAM), Phase-changememory (PRAM), conductive-bridging RAM (CBRAM),Silicon-Oxide-Nitride-Oxide-Silicon (SONOS), Resistive RAM (RRAM),Racetrack, Nano-RAM (NRAM), or Millipede memory. The main memory 122 maybe based on any of the above described memory chips, or any otheravailable memory chips capable of operating as described herein. In theembodiment shown in FIG. 1C, the processor 121 communicates with mainmemory 122 via a system bus 150 (described in more detail below). FIG.1D depicts an embodiment of a computing device 100 in which theprocessor communicates directly with main memory 122 via a memory port103. For example, in FIG. 1D the main memory 122 may be DRDRAM.

FIG. 1D depicts an embodiment in which the main processor 121communicates directly with cache memory 140 via a secondary bus,sometimes referred to as a backside bus. In other embodiments, the mainprocessor 121 communicates with cache memory 140 using the system bus150. Cache memory 140 typically has a faster response time than mainmemory 122 and is typically provided by SRAM, BSRAM, or EDRAM. In theembodiment shown in FIG. 1D, the processor 121 communicates with variousI/O devices 130 via a local system bus 150. Various buses may be used toconnect the central processing unit 121 to any of the I/O devices 130,including a PCI bus, a PCI-X bus, or a PCI-Express bus, or a NuBus. Forembodiments in which the I/O device is a video display 124, theprocessor 121 may use an Advanced Graphics Port (AGP) to communicatewith the display 124 or the I/O controller 123 for the display 124. FIG.1D depicts an embodiment of a computer 100 in which the main processor121 communicates directly with I/O device 130 b or other processors 121′via HYPERTRANSPORT, RAPIDIO, or INFINIBAND communications technology.FIG. 1D also depicts an embodiment in which local busses and directcommunication are mixed: the processor 121 communicates with I/O device130 a using a local interconnect bus while communicating with I/O device130 b directly.

A wide variety of I/O devices 130 a-130 n may be present in thecomputing device 100. Input devices may include keyboards, mice,trackpads, trackballs, touchpads, touch mice, multi-touch touchpads andtouch mice, microphones, multi-array microphones, drawing tablets,cameras, single-lens reflex camera (SLR), digital SLR (DSLR), CMOSsensors, accelerometers, infrared optical sensors, pressure sensors,magnetometer sensors, angular rate sensors, depth sensors, proximitysensors, ambient light sensors, gyroscopic sensors, or other sensors.Output devices may include video displays, graphical displays, speakers,headphones, inkjet printers, laser printers, and 3D printers.

Devices 130 a-130 n may include a combination of multiple input oroutput devices, including, e.g., Microsoft KINECT, Nintendo Wiimote forthe WII, Nintendo WII U GAMEPAD, or Apple IPHONE. Some devices 130 a-130n allow gesture recognition inputs through combining some of the inputsand outputs. Some devices 130 a-130 n provides for facial recognitionwhich may be utilized as an input for different purposes includingauthentication and other commands. Some devices 130 a-130 n provides forvoice recognition and inputs, including, e.g., Microsoft KINECT, SIRIfor IPHONE by Apple, Google Now or Google Voice Search, and ALEXA byAmazon.

Additional devices 130 a-130 n have both input and output capabilities,including, e.g., haptic feedback devices, touchscreen displays, ormulti-touch displays. Touchscreen, multi-touch displays, touchpads,touch mice, or other touch sensing devices may use differenttechnologies to sense touch, including, e.g., capacitive, surfacecapacitive, projected capacitive touch (PCT), in-cell capacitive,resistive, infrared, waveguide, dispersive signal touch (DST), in-celloptical, surface acoustic wave (SAW), bending wave touch (BWT), orforce-based sensing technologies. Some multi-touch devices may allow twoor more contact points with the surface, allowing advanced functionalityincluding, e.g., pinch, spread, rotate, scroll, or other gestures. Sometouchscreen devices, including, e.g., Microsoft PIXELSENSE orMulti-Touch Collaboration Wall, may have larger surfaces, such as on atable-top or on a wall, and may also interact with other electronicdevices. Some I/O devices 130 a-130 n, display devices 124 a-124 n orgroup of devices may be augment reality devices. The I/O devices may becontrolled by an I/O controller 123 as shown in FIG. 1C. The I/Ocontroller may control one or more I/O devices, such as, e.g., akeyboard 126 and a pointing device 127, e.g., a mouse or optical pen.Furthermore, an I/O device may also provide storage and/or aninstallation medium 116 for the computing device 100. In still otherembodiments, the computing device 100 may provide USB connections (notshown) to receive handheld USB storage devices. In further embodiments,an I/O device 130 may be a bridge between the system bus 150 and anexternal communication bus, e.g. a USB bus, a SCSI bus, a FireWire bus,an Ethernet bus, a Gigabit Ethernet bus, a Fibre Channel bus, or aThunderbolt bus.

In some embodiments, display devices 124 a-124 n may be connected to I/Ocontroller 123. Display devices may include, e.g., liquid crystaldisplays (LCD), thin film transistor LCD (TFT-LCD), blue phase LCD,electronic papers (e-ink) displays, flexile displays, light emittingdiode displays (LED), digital light processing (DLP) displays, liquidcrystal on silicon (LCOS) displays, organic light-emitting diode (OLED)displays, active-matrix organic light-emitting diode (AMOLED) displays,liquid crystal laser displays, time-multiplexed optical shutter (TMOS)displays, or 3D displays. Examples of 3D displays may use, e.g.stereoscopy, polarization filters, active shutters, or autostereoscopy.Display devices 124 a-124 n may also be a head-mounted display (HMD). Insome embodiments, display devices 124 a-124 n or the corresponding I/Ocontrollers 123 may be controlled through or have hardware support forOPENGL or DIRECTX API or other graphics libraries.

In some embodiments, the computing device 100 may include or connect tomultiple display devices 124 a-124 n, which each may be of the same ordifferent type and/or form. As such, any of the I/O devices 130 a-130 nand/or the I/O controller 123 may include any type and/or form ofsuitable hardware, software, or combination of hardware and software tosupport, enable or provide for the connection and use of multipledisplay devices 124 a-124 n by the computing device 100. For example,the computing device 100 may include any type and/or form of videoadapter, video card, driver, and/or library to interface, communicate,connect or otherwise use the display devices 124 a-124 n. In oneembodiment, a video adapter may include multiple connectors to interfaceto multiple display devices 124 a-124 n. In other embodiments, thecomputing device 100 may include multiple video adapters, with eachvideo adapter connected to one or more of the display devices 124 a-124n. In some embodiments, any portion of the operating system of thecomputing device 100 may be configured for using multiple displays 124a-124 n. In other embodiments, one or more of the display devices 124a-124 n may be provided by one or more other computing devices 100 a or100 b connected to the computing device 100, via the network 104. Insome embodiments software may be designed and constructed to use anothercomputer's display device as a second display device 124 a for thecomputing device 100. For example, in one embodiment, an Apple iPad mayconnect to a computing device 100 and use the display of the device 100as an additional display screen that may be used as an extended desktop.One ordinarily skilled in the art will recognize and appreciate thevarious ways and embodiments that a computing device 100 may beconfigured to have multiple display devices 124 a-124 n.

Referring again to FIG. 1C, the computing device 100 may comprise astorage device 128 (e.g. one or more hard disk drives or redundantarrays of independent disks) for storing an operating system or otherrelated software, and for storing application software programs such asany program related to the software 120. Examples of storage device 128include, e.g., hard disk drive (HDD); optical drive including CD drive,DVD drive, or BLU-RAY drive; solid-state drive (SSD); USB flash drive;or any other device suitable for storing data. Some storage devices mayinclude multiple volatile and non-volatile memories, including, e.g.,solid state hybrid drives that combine hard disks with solid statecache. Some storage device 128 may be non-volatile, mutable, orread-only. Some storage device 128 may be internal and connect to thecomputing device 100 via a bus 150. Some storage device 128 may beexternal and connect to the computing device 100 via a I/O device 130that provides an external bus. Some storage device 128 may connect tothe computing device 100 via the network interface 118 over a network104, including, e.g., the Remote Disk for MACBOOK AIR by Apple. Someclient devices 100 may not require a non-volatile storage device 128 andmay be thin clients or zero clients 102. Some storage device 128 mayalso be used as an installation device 116 and may be suitable forinstalling software and programs. Additionally, the operating system andthe software can be run from a bootable medium, for example, a bootableCD, e.g. KNOPPIX, a bootable CD for GNU/Linux that is available as aGNU/Linux distribution from knoppix.net.

Client device 100 may also install software or application from anapplication distribution platform. Examples of application distributionplatforms include the App Store for iOS provided by Apple, Inc., the MacApp Store provided by Apple, Inc., GOOGLE PLAY for Android OS providedby Google Inc., Chrome Webstore for CHROME OS provided by Google Inc.,and Amazon Appstore for Android OS and KINDLE FIRE provided byAmazon.com, Inc. An application distribution platform may facilitateinstallation of software on a client device 102. An applicationdistribution platform may include a repository of applications on aserver 106 or a cloud 108, which the clients 102 a-102 n may access overa network 104. An application distribution platform may includeapplication developed and provided by various developers. A user of aclient device 102 may select, purchase and/or download an applicationvia the application distribution platform.

Furthermore, the computing device 100 may include a network interface118 to interface to the network 104 through a variety of connectionsincluding, but not limited to, standard telephone lines LAN or WAN links(e.g., 802.11, T1, T3, Gigabit Ethernet, Infiniband), broadbandconnections (e.g., ISDN, Frame Relay, ATM, Gigabit Ethernet,Ethernet-over-SONET, ADSL, VDSL, BPON, GPON, fiber optical includingFiOS), wireless connections, or some combination of any or all of theabove. Connections can be established using a variety of communicationprotocols (e.g., TCP/IP, Ethernet, ARCNET, SONET, SDH, Fiber DistributedData Interface (FDDI), IEEE 802.11a/b/g/n/ac CDMA, GSM, WiMAX and directasynchronous connections). In one embodiment, the computing device 100communicates with other computing devices 100′ via any type and/or formof gateway or tunneling protocol e.g. Secure Socket Layer (SSL) orTransport Layer Security (TLS), or the Citrix Gateway Protocolmanufactured by Citrix Systems, Inc. of Ft. Lauderdale, Fla. The networkinterface 118 may comprise a built-in network adapter, network interfacecard, PCMCIA network card, EXPRESSCARD network card, card bus networkadapter, wireless network adapter, USB network adapter, modem or anyother device suitable for interfacing the computing device 100 to anytype of network capable of communication and performing the operationsdescribed herein.

A computing device 100 of the sort depicted in FIGS. 1B and 1C mayoperate under the control of an operating system, which controlsscheduling of tasks and access to system resources. The computing device100 can be running any operating system such as any of the versions ofthe MICROSOFT WINDOWS operating systems, the different releases of theUnix and Linux operating systems, any version of the MAC OS forMacintosh computers, any embedded operating system, any real-timeoperating system, any open source operating system, any proprietaryoperating system, any operating systems for mobile computing devices, orany other operating system capable of running on the computing deviceand performing the operations described herein. Typical operatingsystems include, but are not limited to: WINDOWS 2000, WINDOWS Server2012, WINDOWS CE, WINDOWS Phone, WINDOWS XP, WINDOWS VISTA, and WINDOWS7, WINDOWS RT, and WINDOWS 8 all of which are manufactured by MicrosoftCorporation of Redmond, Wash.; MAC OS and iOS, manufactured by Apple,Inc. of Cupertino, Calif.; and Linux, a freely-available operatingsystem, e.g. Linux Mint distribution (“distro”) or Ubuntu, distributedby Canonical Ltd. of London, United Kingdom; or Unix or other Unix-likederivative operating systems; and Android, designed by Google, ofMountain View, Calif., among others. Some operating systems, including,e.g., the CHROME OS by Google, may be used on zero clients or thinclients, including, e.g., CHROMEBOOKS.

The computer system 100 can be any workstation, telephone, desktopcomputer, laptop or notebook computer, netbook, ULTRABOOK, tablet,server, handheld computer, mobile telephone, smartphone or otherportable telecommunications device, media playing device, a gamingsystem, mobile computing device, or any other type and/or form ofcomputing, telecommunications or media device that is capable ofcommunication. The computer system 100 has sufficient processor powerand memory capacity to perform the operations described herein. In someembodiments, the computing device 100 may have different processors,operating systems, and input devices consistent with the device. TheSamsung GALAXY smartphones, e.g., operate under the control of Androidoperating system developed by Google, Inc. GALAXY smartphones receiveinput via a touch interface.

In some embodiments, the computing device 100 is a gaming system. Forexample, the computer system 100 may comprise a PLAYSTATION 3, orPERSONAL PLAYSTATION PORTABLE (PSP), or a PLAYSTATION VITA devicemanufactured by the Sony Corporation of Tokyo, Japan, a NINTENDO DS,NINTENDO 3DS, NINTENDO WII, or a NINTENDO WII U device manufactured byNintendo Co., Ltd., of Kyoto, Japan, an XBOX 360 device manufactured bythe Microsoft Corporation of Redmond, Wash.

In some embodiments, the computing device 100 is a digital audio playersuch as the Apple IPOD, IPOD Touch, and IPOD NANO lines of devices,manufactured by Apple Computer of Cupertino, Calif. Some digital audioplayers may have other functionality, including, e.g., a gaming systemor any functionality made available by an application from a digitalapplication distribution platform. For example, the IPOD Touch mayaccess the Apple App Store. In some embodiments, the computing device100 is a portable media player or digital audio player supporting fileformats including, but not limited to, MP3, WAV, M4A/AAC, WMA ProtectedAAC, AIFF, Audible audiobook, Apple Lossless audio file formats and.mov, .m4v, and .mp4 MPEG-4 (H.264/MPEG-4 AVC) video file formats.

In some embodiments, the computing device 100 is a tablet e.g. the IPADline of devices by Apple; GALAXY TAB family of devices by Samsung; orKINDLE FIRE, by Amazon.com, Inc. of Seattle, Wash. In other embodiments,the computing device 100 is a eBook reader, e.g. the KINDLE family ofdevices by Amazon.com, or NOOK family of devices by Barnes & Noble, Inc.of New York City, N.Y.

In some embodiments, the communications device 102 includes acombination of devices, e.g. a smartphone combined with a digital audioplayer or portable media player. For example, one of these embodimentsis a smartphone, e.g. the IPHONE family of smartphones manufactured byApple, Inc.; a Samsung GALAXY family of smartphones manufactured bySamsung, Inc; or a Motorola DROID family of smartphones. In yet anotherembodiment, the communications device 102 is a laptop or desktopcomputer equipped with a web browser and a microphone and speakersystem, e.g. a telephony headset. In these embodiments, thecommunications devices 102 are web-enabled and can receive and initiatephone calls. In some embodiments, a laptop or desktop computer is alsoequipped with a webcam or other video capture device that enables videochat and video call.

In some embodiments, the status of one or more machines 102, 106 in thenetwork 104 is monitored, generally as part of network management. Inone of these embodiments, the status of a machine may include anidentification of load information (e.g., the number of processes on themachine, CPU and memory utilization), of port information (e.g., thenumber of available communication ports and the port addresses), or ofsession status (e.g., the duration and type of processes, and whether aprocess is active or idle). In another of these embodiments, thisinformation may be identified by a plurality of metrics, and theplurality of metrics can be applied at least in part towards decisionsin load distribution, network traffic management, and network failurerecovery as well as any aspects of operations of the present solutiondescribed herein. Aspects of the operating environments and componentsdescribed above will become apparent in the context of the systems andmethods disclosed herein.

B. Artificial Intelligence (AI) Network and Environment

An intelligent agent is any system or device that perceives itsenvironment and takes actions to maximize its chances of success at somegoal. The term artificial intelligence is used when a machine mimicscognitive functions such as learning and problem solving. One of thetools used for artificial intelligence is neural networks. Someexemplary types of artificial neural networks may be used for artificialintelligence and machine learning are feedforward neuralnetwork-artificial neuron, radial basis function neural network, kohonenself organizing neural network, recurrent neural network, convolutionalneural network and modular neural network. Examples of other artificialintelligence algorithms and machine learning models include reinforcedlearning, logistic regression, statistical regression, decision trees,linear regression and naïve bayes classified algorithms. Although theterm ‘neural network’ is used in the description of the technology inthis disclosure, it is to be understood that any type of artificialintelligence algorithm, whether or not classified as an artificialneural network, may be used to enable the present technology.

Neural networks are modeled after the neurons in the human brain, wherea trained algorithm determines an output response for input signals. Themain categories of neural networks are feedforward neural networks,where the signal passes only in one direction, and recurrent neuralnetworks, which allow feedback and short-term memory of previous inputevents.

A wide variety of platforms has allowed different aspects of AI todevelop. Advances in deep artificial neural networks and distributedcomputing have led to a proliferation of software libraries, includingDeeplearning4j, which is open-source software released under ApacheLicense 2.0 and supported commercially by Skymind of San Francisco,Calif., and TensorFlow, an artificial intelligence system which isopen-source released under Apache License 2.0, developed by Google, Inc.

Deep learning comprises an artificial neural network that is composed ofmany hidden layers between the inputs and outputs. The system moves fromlayer to layer to compile enough information to formulate the correctoutput for a given input. In artificial intelligence models for naturallanguage processing, words can be represented (also described asembedded) as vectors. Vector space models (VSMs) represent or embedwords in a continuous vector space where semantically similar words aremapped to nearby points (are embedded nearby each other). Two differentapproaches that leverage VSMs are count-based methods and predictivemethods. Count-based methods compute the statistics of how often someword co-occurs with its neighbor words in a large text corpus, and thenmaps these count-statistics down to a small, dense vector for each word.Predictive models directly try to predict a word from its neighbors interms of learned small, dense, embedding vectors.

Neural probabilistic language models are traditionally trained using themaximum likelihood (ML) principle to maximize the probability of thenext word given previous words (or context) based on the compatibilityof the next word with the context. The model is trained by maximizingits log-likelihood on a training set. The objective is maximized whenthe model assigns high probabilities to the words which are desired (thereal words) and low probabilities to words that are not appropriate (thenoise words).

A framework is provided that allows an artificial intelligence machinelearning system to create risk scores, which are a representation ofvulnerability to a malicious attack. In some embodiments, the learnedvalues from a neural network may also be serialized on disk for doingthe inference step at a later time. These learned values may be storedin multidimensional arrays that also contain shape and type informationwhile in memory.

C. Systems and Methods for Creating Vulnerability Scores

The following describes systems and methods of creating vulnerabilityscores, for example using artificial intelligence, for use in a securityawareness system.

A simulated phishing attack may test the readiness of a security systemor users of a system to handle phishing attacks such that maliciousactions are prevented. A simulated phishing attack may, for example,target a large number of users, such as employees of an organization. Atarget is defined as the user for whom the simulated phishing attack isdirected, i.e. the user that is being tested. A simulated phishingmessage is sent to the target's address. The message can masquerade as amessage from a party known to the target, such as an executive of thecompany that employs the target. In some embodiments, the message canappear to be sent from a party unknown to the target. The message may bedesigned to appear interesting to the target and may make an offer orpromise e.g. access to an interesting tidbit of news, access to usefulcomputer software, access to knowledge of how to perform a money-makingscheme, or any other thing that may be of interest. In someimplementations, the message may request that the target perform acertain action, such as providing sensitive information by replying tothe message or transferring money to an account owned by the attackerand then sending a reply message to confirm that the money has beentransferred. The message may request the target to perform any actionthat could result in a security breach if the simulated phishing messagewas a real phishing message.

In some implementations, each message sent to a target may include aunique identifier. For example, a unique identifier may be a series ofnumbers and/or letters, or a unique string of characters included in thesubject of the message, in the body of the message, or in the messageheaders. In some other examples, the unique identifier may be a uniquemessage address, for example in either the “To”, “From:” or the “Cc:”field of an email. In still other examples, the unique identifier may beincluded within a separate file that is attached to the email.

The unique identifier can help to determine the identity of a user whowas first sent the simulated phishing message. In implementations inwhich the unique identifier is included within the subject line of thesimulated attack message, the body of the simulated attack message, inthe message headers of the simulated phishing message or in a fileattached to the simulated phishing message, if the message is forwardedby the target or by any other user, the forwarded message may alsoinclude the unique identifier within the subject, the body, the messageheaders or in the attachment of the original simulated phishing messagethat is forwarded along with the message. In implementations in whichthe unique identifier is included within the subject line of thesimulated phishing message, the body of the simulated phishing message,or in the message headers of the simulated phishing message, the replymessage can be processed to extract the unique identifier from thesubject line of the simulated phishing message, the body of thesimulated phishing message, or in the message headers of the simulatedphishing message. The unique identifier can be used to identify thetarget of the simulated phishing message, regardless of which userultimately sends the simulated phishing message back to the securityawareness system. Thus, even if the simulated phishing message receivedat the system is sent from an address that is not known to be associatedwith a particular target, the unique identifier can help to determinethe identity of the target of the simulated phishing message.

In implementations in which the unique identifier is included within anemail address in the “To:”, “From:” or “Cc:” fields of the simulatedphishing message, a reply to the simulated phishing message sent by thetarget, for example from a different address associated with the target,may also include the unique identifier within the “From:”, “To:” or“Cc:” fields respectively. The reply message can be processed to extractthe unique identifier as discussed above, and the unique identifier canbe used to determine the identity of the target of the original email,even if the reply simulated phishing message is sent from a differentaddress than the address that the security awareness system sent theoriginal simulated phishing message to. Sending a reply message inresponse to receiving a simulated phishing message can be classified asa failure. In embodiments, forwarding a received simulated phishingmessage can be classified as a failure. Therefore, the securityawareness system must be able to identify the original target of thesimulated phishing message. Once the identity of the target has beendetermined, a record of the target's failure can be stored.

A system can be configured to derive a measure of potential risk,individualized for each user. In embodiments, a vulnerability score(also called a risk score) will be based on machine-learned predictiveanalytics and will represent how vulnerable an organization's users are.

In some examples, the risk score derivation will be based on traininghistory, phishing history, responses to simulated phishing tests,demographic information, information about the organization, breachdata, user assessment surveys and data which may be obtained from aSIEM. User assessment surveys may include questions such as, “Underwhich of the following circumstances is it acceptable to share apassword with a co-worker?”. The system may present multiple choiceanswers for the user to choose from. The returned information getsinterpreted as a strength or weakness, and may be used to determinespecific training or simulated phishing templates to send the user.Other questions focus on a user's perceived confidence in differentareas. In embodiments, questions focus on a user's attitude towards, andknowledge of specific security risks or situations. In embodiments, thesystem takes responses to user assessments into account in determiningthe best way to target that user or test their knowledge on a specifictopic.

In some embodiments, a risk score framework is created, which outlinesthe data that is considered in creating the risk score such as thefrequency a user receives phishing attacks, the severity of thoseattacks, and the method of calculating the risk score. In someembodiments, a variety of data sources may be considered in creating therisk score. In some examples, records reflecting user responses to realand simulated phishing attacks may considered in creating the riskscore. The sophistication of the user's response to various real andsimulated phishing attacks may be considered in creating the risk score.In some examples, the sophistication of the user's response to variousreal and simulated phishing attacks may be given a score or a ranking,for example, a user's response may be given a score from 0 or 1,representing the least sophisticated response, to 5, representing themost sophisticated response, and the score or ranking of the user'sresponse to various real and simulated attacks may be considered increating the risk score. In embodiments, user training recordsconsidered in creating the risk score. For example, the training thatthe user has completed, the time spent engaged in training activities,the duration of the training modules that the user has completed, andother details related to the training or learning related to maliciousattacks that the user has undertaken may be considered in creating therisk score.

In some embodiments, user demographics are integrated as sources of datathat considered in creating the risk score. For example, the user's age,gender, and tenure at a current job may be considered in creating therisk score. In some embodiments, the user's organizational unit, jobtitle, and manager may be considered in creating the risk score. In someexamples, the user's membership in distribution lists or groups may betaken into consideration in calculating the risk score. In embodiments,information about data breaches related to the user or to theorganization may be considered in creating the risk score. Theaforementioned are non-limiting examples of the types of data related toa user that may be considered in creating the risk score. Inembodiments, the data may be integrated into data sets used to trainmachine learning models, the machine learning models configured topredict user responses to malicious attacks based on the integrateddata. In some embodiments, data collection is performed on an ongoingbasis, and updated data and/or data sets may be used to re-train machinelearning models or create new machine learning models that evolve as thedata changes.

In embodiments, the risk score of an individual may be represented as:Risk Score (RS)=f{f(H),p(R|H),s(R,H)}=RS(f,p,s)where:H=a hit, de fined as any kind of malicious attackf(H)=frequency of potential harmful hits Hp(R|H)=the propensity that an individual will respond to a hit HR=individual response, e.g. a click, reply, etc.s(R,H)=the severity of the impact of response R to hit Hsubject to constraints:RS(0,0,0)≥0RS(1,1,1)=100In some embodiments, RS(f, p, s) is an increasing function of each ofits variables: f, p, and s.

In some embodiments, responses can include a multitude of user actions,for example but not limited to a user opening a message, clicking on alink in a message, replying to a message, opening an attachment to amessage, enabling a macro in a message, entering data in response to amessage, reporting a message, or forwarding a message. In some examples,data about responses may be aggregated, or temporal information may beincluded, for example the number of days since a user last responded, orhow many instances of each type of response (or any type of response) inthe last e.g. 30 days, 3 months, 1 year, etc.

In some embodiments, the training and learning history of a user may bepartitioned based on the type of course or module that the userperformed. In some examples, the training history may be divided intoshort modules (less than a predetermined duration) or long modules(greater than a predetermined duration). In some examples, traininghistory may be divided based on the type of training, for exampleclassroom training or online training. Training history may includecourses that the user is enrolled in by the system, courses that theuser has chosen to enroll in voluntarily, courses that the user hasstarted, or courses that the user has completed. Training history mayinclude the time interval between completed training courses.

In some embodiments, user data may be arranged in a tabular format,whereby the rows of the table represent a phish instance for a userwhich may include a detailed representation of the user and theirphishing and training history at a given point of time. In someembodiments, when training a risk score model, the system usesinformation in a table to learn how the user responded to a specificattack given their history at the time of the attack.

The first component of the risk score calculation is f(H), whichreflects the frequency at which individuals are hit with a maliciousattack (H). In some embodiments, this information is based on twoproxies in the data, job score and breach score. In some embodiments,job score may be defined as follows:Job Score (job title)=J=(0,5,6,10)where:0=does not match a category or no information available5=accounting or IT6=high level, e.g. manager, director, lead10=executive, e. g. C×OIt is understood that these example job classifications aredemonstrative, and any job classifications may be used, and more orfewer job scores may be enabled.

In some embodiments, a breach score may be based on an email exposurecheck (EEC) threat level, for example a breach score may be defined asfollows:Breach Score (EEC)=B=(0,3,10)where:0=the user has negligable email exposure3=the user has moderate email exposure10=the user has high email exposureIt is understood that these example breach scores are demonstrative, andany breach scores may be used, and more or fewer breach scores may beenabled.

In some embodiments, breach score information may be decayed over time.In some examples, the data supporting the breach score inputs may besparse, or in some examples the users may not have a job title whichfits into any of these categories, in which case f(H) may take on abaseline value.

The first component of the risk score calculation is the severity s(R,H) which reflects the severity of the user response R to maliciousattack or hit H. In some embodiments s(R, H) may be a function ofindividual access. In some embodiments, the severity may be a functionof the user's job score. In some embodiments, the severity may by afunction of a risk booster value, which may be set by a company orsystem administrator to customize the assessed risk of individuals or ofgroups of individuals. In some embodiments, severity score may bedefined as follows:s(R,H)=(−1,0,1,10)where:−1=below normal risk0=normal risk (default)1=elevated risk10=very high riskIt is understood that these example severity score classifications aredemonstrative, and any severity score classifications may be used, andmore or fewer severity scores may be enabled.

Although a lot of functions satisfy these criteria, a natural family ofcandidates would need to convey the multiplicative nature of RS. Thecomponents of RS—f, p, and s, represent the expected loss due tomalicious attacks over a period of time. The function RS(f, p, s) insome embodiments may be represented as a weighted sum of logarithms:RS(f,p,s)=w ₁ log f+w ₂ log p+w ₃ log s

In some embodiments, the function RS(f, p, s) may be represented asfollows:

${{RS}\left( {f,p,s} \right)} = {{w_{1}{\log\left( {1 + J + B} \right)}} + {w_{2}{\log\left( {1 + {P\left( R \middle| H \right)}} \right)}} + \frac{w_{3}{\log\left( {\left( {1 + J} \right)\left( {1.5 + \frac{RB}{2}} \right)} \right)}}{d}}$where:w_(i)=settable parameters, normalized so that 0≤RS≤100J=job score (0, 5, 6, or 10)B=breach score (0, 3, or 10)RB=severity (s)(−1,0,1,10)and:

$d = {\frac{\log\left( {21 + {100{\log(2)}} + {\log\left( {11 \times 6.5} \right)}} \right)}{100} = {{0.77\mspace{14mu}{when}\mspace{14mu} w_{i}} = 1}}$

In some embodiments, the propensity of RS, p(R|H) represents thepredictive model component of the risk score RS. In some examples, thisvalue may be produced by:

a. Training statistical and neural network models to learn the mappingfrom a particular user history (input features) to whether or not theuser responded (response R) on a given malicious attack (H).

b. After repeated exposure to millions of samples, the models are ableto learn to predict with some success whether or not users with givenmalicious attack and training histories are likely to perform a responseR given an attack H.

c. Given enough training data, models are able to predict a variety ofdifferent user responses R to a variety of different attacks H.

In some embodiments, the model's output, p(R|H) is the probability thata given user will respond, for example click, in response to a maliciousattack, for example a phishing email, at a particular point in time. Thesame user may exhibit a different p at a later time. To evaluate thepredictive performance of the model, we need more than one maliciousattack, which allows the model to predict the click rate of a user overa period of time and compare it to the user's actual click rate overthat period of time. For example, assume the period of time is one year.For each user, calculate the predicted p(R|H) each time a phishing emailhits over one year and record whether or not the user clicked. p(R|H) isharder to predict and more variable for users with few phish events. Themore phish data that is available about a user, the more accurate therisk score RS becomes. In some embodiments, the performance of the modelis assessed by predicting the number of clicks instead of the percentageover a period of time and comparing it to the user's actual clicks overthat period of time.

The value of p(R|H) is a very good predictor in the aggregate for agroup of users with similar profiles. When users are categorized bytheir predicted p(R|H) value, the percent of actual clicks in eachcategory closely tracks the predicted p(R|H) value for the category.Uncertainties at the individual level get smaller when groups ofindividuals are aggregated. The propensity component p(R|H) of the riskscore RS becomes more predictive as the number of phish eventsincreases. In some embodiments, this can be achieved by taking multipleevents into account for each user. In embodiments, this can be achievedby aggregating the expected click rate at the level of a group. The useof the highest individual risk score in a group at the risk score forthe group is not recommended as it puts too much weight on one singleestimate. The average over all individual risk scores in the group isone possible approach to aggregating the risk scores, however this maytend to underestimate the contribution of isolated outliers. In someexamples, the approach taken may be referred to as using the standarddistance to the perfect score (0), described as follows:

${{RS}({group})} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {{RS}(i)}^{2} \right)}}$where:N=number of group membersRS(i)=individual risk score of member iAs an example, consider the case where N=100, RS(1)=100, and RS(i)=0,i=2: 100. Then, RS_(max)=100, RS_(average)=1, and RS_(group)=10.

In some embodiments, user training histories are used in predictingp(R|H) values. When more users have had training, p(R|H) is low, andwhen fewer users have had training, p(R|H) is high. In some examples,when users have had training recently, p(R|H) is low.

In some embodiments, several different processes or workflows areinvolved in the prediction of risk score. In some embodiments, data aredivided up into groups, and a percentage of the data is used fortraining, a percentage of the data is used for validation, and apercentage of the data is used for text. In one examples, 80% of thedata is used for training, 10% of the data is used for validation, and10% of the data is used for test. In some embodiments, data fromarchived users may be used for any of training, validation or testing ofthe model. In some embodiments, the model is updated, trained orretrained periodically as new data enters the system. In someembodiments, this may be updated daily, weekly, monthly, or yearly. Insome embodiments, users may be added to or removed from groups that areused to train the models. For example, a user may more from one joblevel to a different job level, and all users of a given job level arebeing used to train a model for use with that job level. In thisexample, the user that moved job level would be removed from the groupof users used to train, validate or test the model for that job level.

Referring to FIG. 2 in a general overview, FIG. 2 depicts some of thearchitecture of a security awareness system 200 capable of determiningvulnerability scores for malicious cyberattacks using artificialintelligence, and of calculating metrics in a security awareness systemwhile ensuring that the results of simulated phishing attacks areattributed to the correct user.

In some embodiments, system 200 is configured to calculat metrics and/orvulnerability scores and is capable of using calculated metrics and/orvulnerability scores to inform individualized user or group usertraining. In some embodiments, calculated metrics and/or vulnerabilityscores may be used to inform individualized and group reporting. In someimplementations, the system 200 includes one or more servers 106, one ormore clients 102, and one or more security services providers 210.Server 106 may include administrator console 295, while may includemetrics generator 296, phish-prone percentage calculator 297, anddashboard generator 298, and user input processor 299. Server 106 mayinclude user group manager 220, which may include user group selector222, user group management application 224, phishing message userinteraction counter 226, and user remediation training completioncounter 228. Server 106 may include simulated phishing campaign manager230, which may include a storage for simulated phishing messages 232,event tracker 234, phishing message interaction tracker 236, userinterface manager 238, and simulated phishing message generator 240,which may include virtual machine 242. Server 106 may include learningmanagement manager 250 which may include training selector 252,remediation training tracker 254, and remediation training storage 256.Server 106 may also include storages for users 241 and user groups 242.

System 200 may include client 102, which may include communicationsmodule 264, user interface 266, display 268, and messaging application270. System 200 may also include security services provider 210, whichmay include real time monitoring 218, security records 212, long termstorage 216, and external threat data 214. System 200 may includenetwork 104 allowing communication between these system components.

Referring to FIG. 2 in more detail, simulated phishing campaign manager230 may be e.g., a security manager, a third-party security consultant,a risk assessor, or any other party. Simulated phishing campaign manager230 may wish to direct a simulated phishing attack by interacting withuser group manager 220 and client 102 through simulated phishingcampaign manager 230 installed on a device. The device may be, forexample, a desktop computer, a laptop computer, a mobile device, or anyother suitable computing device. The simulated phishing campaign manager230 may be e.g., an application on a device that allows for a user ofthe device to interact with the simulated phishing campaign manager 230for e.g. purposes of tailoring and/or executing a simulated phishingattack. Administrator console 295 may allow a user, also known as anadministrator, to view and/or process and/or analyze the results of asimulated phishing attack. Administrator console 295 may interact withevent tracker 234, phishing message interaction tracker 236, phishingmessage user interaction counter 226, user remediation trainingcompletion counter 228, and remediation training tracker 254 to allow anadministrator to view and/or process and/or analyze historical behaviorsor users and/or groups with respect to real and simulated phishingattacks. Administrator console 295 may interact with security servicesprovider 210, which may include real time monitoring 218, securityrecords 212, and external threat data 214 that are related to users thatare managed by the system 200.

In an implementation, simulated phishing campaign manager 230, whenexecuted on the device, causes e.g. a graphical user interface to bedisplayed to e.g. the simulated phishing campaign manager 230. In otherimplementations, the administrator console 295 allows for user inputthrough a non-graphical user interface, e.g. a user interface thataccepts text or vocal input without displaying an interactive image. Agraphical user interface may be displayed on a screen of a mobile phone,or a monitor connected to a desktop or laptop computer or may bedisplayed on any other display. The user may interact with e.g. thegraphical user interface on the device by typing, clicking a mouse,tapping, speaking, or any other method of interacting with a userinterface. The graphical user interface on the device may be a web-baseduser interface provided by a web browser (e.g. GOOGLE CHROME, MicrosoftINTERNET EXPLORER, or Mozilla Firefox provided by Mozilla Foundation ofMountain View, Calif.), or may be an application installed on a devicecapable of opening a network connection to simulated phishing campaignmanager 230 or to the administrator console 295 or may be any other typeof interface.

In an implementation, the simulated phishing campaign manager 230 maymake choices concerning how a simulated phishing attack is to be carriedout. For example, a graphical user interface run by the administratorconsole 295 may be displayed to the simulated phishing campaign manager230 on a display of the device. The simulated phishing campaign manager230 may input parameters for the attack that affect how it will becarried out. For example, simulated phishing campaign manager 230 mayinteract with user group manager 220 to make choices as to which usersfrom users storage 241 or which user groups from user groups storage 242to include as potential targets in the attack. User group manager 220may control the method of determining which users are to be selected astargets of the attack, and simulated phishing campaign manager 230 maycontrol the timing of various aspects of the attack, whether to use anattack template that includes values for one or a plurality of attackparameters, how responses from targeted users should be uniquelyidentified, and other choices. These choices may be made by e.g.selecting options displayed on a graphical user interface from dropdownmenus, being presented with choices through a simulated attack wizard,or in any other appropriate manner.

In an implementation, the simulated phishing campaign manager 230 mayallow the administrator console 295 to access and/or change settings ofan account maintained with any party involved with the attack, such as,for example, a third-party security service provider, or a party thatmanages interactions with a third-party security services provider 210to access and/or change settings of an account maintained with athird-party security services provider 210. Simulated phishing campaignmanager 230 may manage various aspects of a simulated phishing attack.For example, simulated phishing campaign manager 230 may process inputfrom the administrator console 295, may provide access as needed tovarious applications, modules, and other software components of thesecurity awareness server 106 to other various applications, modules,and other software components of the simulated phishing campaign manager230, may monitor and control timing of various aspects of a simulatedattack, may process requests for access to attack results, or mayperform other tasks related to the management of a simulated attack.

In an implementation, system 200 includes a security awareness systemserver 106. The security awareness system server 106 may be a part of acluster of servers. In some implementations, tasks performed by thesecurity awareness system server 106 may be performed by a plurality ofsecurity awareness system servers. These tasks may be allocated amongthe cluster of servers by an application, service, daemon, routine, orother executable logic for task allocation. The security awarenesssystem server 106 may include a processor and memory.

In some implementations, simulated phishing campaign manager 230 oradministrator console 295 may include a user input processor. The userinput processor may receive input from e.g. and administrator using e.g.the administrator console 295 to manage a simulated phishing attack. Theuser input processor may be, for example, a library, applicationprogramming interface (API), set of scripts, or any other code that maybe accessed by, or executed via a network connection by, or providecallback methods for, simulated phishing campaign manager 230.

In an implementation, the user input processor may be integrated withthe memory. The memory may store data such as parameters and scriptsassociated with a particular simulated phishing attack. For example, thememory may store a set of parameters and scripts corresponding to thechoices made by a simulated phishing campaign manager 230 for aparticular simulated phishing attack.

In an implementation, simulated phishing campaign manager 230 includessimulated phishing message generator 240. Simulated message generator240 may be integrated with the memory so as to provide the simulatedphishing message generator 240 accesses to parameters associated withmessaging choices made for a particular simulated attack by e.g. thesimulated phishing campaign manager 230. The simulated phishing messagegenerator 240 may be an application, service, daemon, routine, or otherexecutable logic for generating messages. The messages generated bysimulated phishing message generator 240 may be of any appropriateformat. For example, they may be email messages, text messages, messagesused by particular messaging applications such as, e.g., WhatsApp™, orany other type of message. Message type to be used in a particularattack may be selected by e.g. a simulated phishing campaign manager230. The messages may be generated in any appropriate manner, e.g. byrunning an instance of an application that generates the desired messagetype, such as running e.g. a Gmail™ application, Microsoft Outlook™,WhatsApp™, a text messaging application, or any other appropriateapplication. The messages may be generated by running a messagingapplication on e.g. a virtual machine 242 or may simply be run on anoperating system of the security awareness system server 106 or may berun in any other appropriate environment.

In some implementations, the simulated phishing message generator 240may be configured to generate messages having characteristics thatfacilitate identification of targeted users. In some embodimentssimulated phishing message generator 240 may include a unique identifierin each simulated attack message sent to a target. In examples, messageapplication 270 of client 102 may include a unique identifier in eachsimulated phishing message that client 102 that is the targeted usereither replies to or forwards to a different client 102.

In some implementations, each simulated phishing message sent to atarget may include a unique identifier. In embodiments, a uniqueidentifier may be a series of numbers and/or letters, or a unique stringof characters. In some examples, the unique identifier may be includedin the subject of the message or in the body of the message. Inembodiments, the unique identifier may be included in the header of thesimulated phishing message. In examples, the unique identifier may be aunique email address in either the “From:” field or the “Cc:” field ofthe email. In still other examples, the unique identifier may beincluded within a separate file that is attached to the email.

The unique identifier can help to determine the identity of the targetuser for a simulated phishing message if the user fails the simulatedphishing test. In examples, a user who replies to a simulated phishingmessage is considered by simulated phishing campaign manager 230 to havefailed the simulated phishing test. In some embodiments, a user whoforwards a simulated phishing message is considered by simulatedphishing campaign manager 230 to have failed the simulated phishingtest. In implementations in which the unique identifier is included, forexample, within the header of the simulated phishing message, thesubject line of the simulated phishing message, the body of thesimulated phishing message, or a file attached to the simulated phishingmessage, a reply email received by simulated phishing campaign manager230 may also include the unique identifier within the header, thesubject, the body, or an attachment of the reply to the simulatedphishing message. The reply to the simulated phishing message can beprocessed by phishing message interaction tracker 236 to extract theunique identifier, and the unique identifier can be used to identify theoriginal target of that simulated phishing communication. Thus, even ifthe reply to the simulated phishing communication is sent from an emailaddress that is not known to be associated with the original target ofthat simulated phishing message, the unique identifier may be used todetermine the identity of the target who replied to the email such thatthe failure may be attributed to the correct user.

Similarly, in implementations in which the unique identifier is includedwithin a header of the simulated phishing communication, for example inthe “From:” or “Cc:” fields of the header of the simulated phishingcommunication, a reply message sent by the target also will include theunique identifier within the “To:” or “Cc:” fields. The simulatedphishing campaign manager 230 receives the reply message and may extractthe unique identifier from the “To:” or “Cc:” fields, and the uniqueidentifier can be used to determine the identity of the original targetof the simulated phishing email.

In some implementations, the simulated phishing message may not includea unique identifier in the “From:” or “Cc:” fields, in any header field,subject line, or body of the simulated phishing message, or in anattachment to the simulated phishing message. Instead, client 102 maycreate, modify, or append to the reply message address (i.e., the “To:”field of the reply email a unique identifier, and the reply email may beprocessed by simulated phishing campaign manager 230 to extract theunique identifier to determine the identity of the original target ofthe simulated phishing email message. Sending a reply or forwarding athe simulated attack email, in response to receiving the simulatedattack email, can be classified as a failure. Therefore, after theidentity of the original target has been determined, a record of thetarget's failure can be stored.

In some implementations, a simulated phishing message can be sent to alarge number of users. In such a situation, it may be difficult orimpossible to determine the identity of the target of the simulatedphishing message if when the simulated communication message is receivedat simulated phishing campaign manager 230. For example, if the targetuser replies to the simulated phishing message from an email accountdifferent from the account to which the simulated phishing message emailwas originally sent, then the simulated phishing campaign manager 230cannot determine the original target through inspect of the “From:”field of the received message. In one example, a company may send asimulated phishing message to each of its employees. In embodiments,each simulated phishing message may be identical or nearly identicalexcept for unique identifiers specific to each employee that are addedby simulated phishing message generator 240 to the simulated phishingmessage. In one example, a simulated phishing message may be sent to thebusiness email addresses of an employee, and the employee may reply tothe simulated phishing message from their personal email account. Theunique identifiers in the simulated pushing messages can facilitate theidentification of the original target of the simulated phishing messageregardless of the email addresses from which simulated phishing campaignmanager 230 receives the message.

In some implementations, simulated phishing message generator 240 can beconfigured to generate a simulated phishing email. The simulatedphishing message can have a “Subject:” field that is intended to because the recipient to take an action, such as initiating a wiretransfer. In some implementations, the simulated phishing messagegenerator 240 can generate multiple instances of the email which may bedelivered to multiple users, such as a subset of all of the employees ofthe company. For example, the simulated phishing campaign manager 230can select any number of employees who should be targeted by a simulatedattack, and parameters corresponding to the identities of the selectedtargets can be stored in the memory. Simulated phishing messagegenerator 240 can retrieve this information from the memory and cangenerate a set of simulated phishing messages, each addressed to arespective user identified in the information stored in the memory. Thatis, simulated phishing message generator 240 can generate the simulatedphishing messages such that the “From:” and “Subject:” fields of eachsimulated phishing message are identical, while the “To:” field and theunique identifier for each simulated phishing message is adjustedaccording to the desired user.

After the client 102 has received a simulated attack message, client 102can send a reply message. For example, client 102 may reply to thesimulated attack message with a reply message to inform the sender ofthe simulated attack message that client 102 has completed a requestedaction, such as initiating a wire transfer. In another example, client102 may reply to the simulated attack message with the reply message toprovide other sensitive information to the sender of the simulatedattack message. In some examples, the reply message to the simulatedattack message includes the same “Subject:” field as the simulatedattack message, however, the “To:” field of the reply message includes aunique identifier. In some implementations, the unique identifier can beincluded in the local part of the “To:” field of the reply message(i.e., the portion of the “To:” field of the reply message before the“@” character). In other implementations, the unique identifier can beincluded in the domain part of the “To:” field of the reply message(i.e., the portion of the “To:” field of the reply message after the “@”character). In still other implementations, each of the local part andthe domain part of the “To:” field of the reply message can includeunique identifiers. A unique identifier can be e.g., a string ofletters, numbers, or special characters associated only with aparticular user. Thus, all potential user can have a different uniqueidentifier. After the reply message is sent by client 102, it can bereceived by the simulated phishing campaign manager 230.

The system 200 includes also the client 102. Client 102 may also bereferred to as user 102, and may also be a client device or user device,and the terms client and client device may be used interchangeably. Auser in the system may be referred to as a client or a client device, ora target or a target device. As described above, a user may be anytarget of a simulated phishing attack. For example, the target may be anemployee, member, or independent contractor working for an organizationthat is e.g. performing a security checkup or conducts ongoing simulatedphishing attacks to maintain security. Target device 102 may be anydevice used by the target of the simulated phishing attack. The targetneed not own the device for it to be considered a target device 102.Target device 102 may be any computing device, e.g. a desktop computer,a laptop, a mobile device, or any other computing device. In someimplementations, target device 102 may be a server or set of serversaccessed by the target. For example, the target may be the employee or amember of an organization. The target may access a server that is e.g.owned or managed or otherwise associated with the organization. Such aserver may be target device 102.

In some implementations, the target device 102 may include a processorand memory. The target device 102 may further include a user interface266 such as, e.g., a keyboard, a mouse, a touch screen, or any otherappropriate user interface. This may be a user interface that is e.g.connected directly to target device 102, such as, for example, akeyboard connected to a mobile device, or may be connected indirectly toa target device 102, such as, for example, a user interface of a clientdevice used to access a server target device 102. The target device 102may include display 268, such as, e.g., a screen, a monitor connected tothe device in any manner, or any other appropriate display.

In an implementation, the target device 102 may include a messagingapplication 270. Messaging application 270 may be any applicationcapable of viewing, editing, and/or sending messages. For example,Messaging application 270 may be an instance of an application thatallows viewing of a desired message type, such as any web browser, aGmail™ application, Microsoft Outlook™, WhatsApp™, a text messagingapplication, or any other appropriate application. In someimplementations, messaging application 270 can be configured to displaysimulated attack emails. Furthermore, messaging application 270 may beconfigured to allow the target to generate reply messages in response tosimulated attack messages displayed by messaging application 270. Forexample, messaging application 270 may be configured to allow the targetto forward simulated attach messages displayed by messaging application270 s.

In some implementations, the target device 102 may include acommunications module 264. This may be a library, applicationprogramming interface (API), set of scripts, or any other code that mayfacilitate communications between the target device 102 and any ofsecurity awareness system server 106, security services provider 210, orany other server. In some implementations, communications module 264determines when to transmit information from target device 102 toexternal servers via network 104. In some implementations, theinformation transmitted by communications module 264 may correspond to asimulated attack message, such as an email, generated by messagingapplication 270.

In implementations, the security awareness system server 106 includeslearning management manager 250. Learning management manager 250 mayinclude a database of remediation training 256. Database 256 may beintegrated with learning management manager 250. In some embodiments,learning management manager 250 includes remediation training tracker254, which may be configured to keep track of the training undertaken byeach user in system 200. Remediation training tracker 254 may beconfigured to maintain a schedule of upcoming training to be undertakenby a user in the system. Remediation training database 256 may includetraining modules that are available for selection for training for usersof system 200. In some embodiments, remediation training database 256includes pointers to external training offerings which are not stored inremediation training database 256, but which are stored elsewhere inserver 106 or as part of a third-party server or service. Beingcorrectly able to identify the target user of a simulated phishingcommunication message may guide learning manager 250 to choose specificremedial training that addresses the mistake that the target user madewhich lead the target user failing the simulated phishing test.

In some implementations, system 200 includes security services provider210. In some embodiments, this functionality is referred to as asecurity information management (SIM) system, security event management(SEM) system, or security information and event management (SIEM)system. Security information management (SIM), security event management(SEM), or security information and event management (SIEM) is thepractice of collecting, monitoring and analyzing security-related datafrom computer logs, including event log data from security devices, suchas firewalls, proxy servers, intrusion detection systems, intrusionprevention systems, file systems, routers, switches, and antivirussoftware. A security information management system (SIMS), securityevent management system (SEMS) or security information and eventmanagement system (SIEMS), herein collectively referred to as a securityinformation and event management system (SIEMS), automates the processof collecting, monitoring and analyzing security-related data fromcomputer logs, including event log data from security devices, such asfirewalls, proxy servers, intrusion detection systems, intrusionprevention systems, file systems, routers, switches, and antivirussoftware and translates the logged data into correlated and simplifiedformats. SIEMS may monitor events in real-time, display a real-time viewof activity, translate event data from various sources into a commonformat, typically XML or JSON, aggregate data, correlate data frommultiple sources, cross-correlate to help administrators discern betweenreal threats and false positives, provide automated incidence response,and send alerts and generate reports. SIEMS collect and centrally managerecords of network, application, device, security, and user activityfrom different infrastructure sources. Reports may be defined for aSIEM, and an SIEM may also provide query functionality for both realtime and historical records.

Commercial SIEM products include ArcSight ESM (Micro Focus, Newbury,England), nFX's SIM One (netForensics inc., Edison, N.J.), envision(Network Intelligence, Westwood, Mass.), EventTracker (EventTracker,Fort Lauderdale, Fla.), Trigeo (TriGeo Network Security, Post Falls,Id.), Symantec's Security Information Manager (Symantec, Mountain View,Calif.), Cisco Security Manager, MARS (Cisco, San Jose, Calif.), andSnare (Snare Solutions, Adelaide, Australia). Open source SEIM productsinclude OSSIM, a product of the Open Source Security InformationManagement initiative, and Prelude, from PreludeIDS. SEIMS may usenormalization, which means automatically pulling common data items fromeach event (like who, what, when and where) and storing this subset ofinformation into a common format. Security awareness systems can usethis common format to find records which may be relevant to users in thesecurity awareness system.

Metasploit (Rapid7), Canvas (Immunity) and Core Impact (Core SecurityTechnologies) are examples of vulnerability testing systems that can beused to test the vulnerability of computer systems, or to break intoremote systems. Metasploit 3.0 introduces fuzzing tools which are usedto discover software vulnerabilities. Metasploit can be used bysecurities teams who need to identify vulnerabilities in software andsystems, which their users may be exposed to.

Mimecast (London, UK) is an example of a secure email gateway thatprovides cloud-based anti-virus and anti-spam protection as well as manyother security services such as DNS Authentication services, includingSender Policy Framework (SPF), DomainKeys Identified Mail (DKIM) andDomain Message Authentication Reporting and Conformance (DMARC) toaddress sender spoofing, prevention against impersonation attacks,prevention against the delivery of malicious attachments through theapplication of multiple signature-based, static and sandboxing securityinspections, protection against malicious URLs, whether they are luresto phishing or malware drop sites.

SIEMS, vulnerability testing systems, and secure email gateways can allproduce information about the threats that an organization or anindividual has been exposed to, which may not be apparent to thesecurity awareness system. In some embodiments, a user may use systemsor software that have been determined to be vulnerable by avulnerability testing system. In some embodiments, a user may havereceived a number of attacks which have been stopped by a secure emailgateway before they reached the company's email system. In someembodiments, a user may be recognized within a SIEM to be associatedwith one or more multiple security threats or incidents that arerecorded by the SIEM.

In some embodiments, security services provider 210 is a securityappliance. Security services provider 210 may be used to refer to any ofthe aforementioned embodiments as well as other embodiments of securityservices that perform the functionality presently described. Securityservices provider 210 may monitor events in real-time, display areal-time view of activity, translate event data from various sourcesinto a common format, typically XML or JSON, aggregate data, correlatedata from multiple sources, cross-correlate to help administratorsdiscern between real threats and false positives, provide automatedincidence response, and send alerts and generate reports. Securityservices provider 210 may collect and centrally manage records ofnetwork, application, device, security, and user activity from differentinfrastructure sources. Reports may be defined for security servicesprovider 210, and security services provider 210 may also provide queryfunctionality for both real time and historical records.

In the systems and methods of the present invention, security awarenesssystem server 106 may receive information from any of the aforementionedsystems and may use this information to generate additional reports andmetrics related to users in the security awareness system. An API couldbe provided to enable the security awareness system to receive theinformation from the aforementioned systems in a common format. In someembodiments, a security awareness system server 106 may use informationfrom any of the aforementioned systems in a learning management system,in order to tailor training to a specific individual.

Security services provider 210 may also include vulnerability testingsystems that can be used to test the vulnerability of computer systems,or to break into remote systems. Examples of vulnerability testingsystems include Metasploit (RSIMSd7), Canvas (Immunity) and Core Impact(Core Security Technologies). As a further example, Metasploit 3.0introduces fuzzing tools which are used to discover softwarevulnerabilities. Metasploit can be used by securities teams who need toidentify vulnerabilities in software and systems, which their users maybe exposed to.

Security services provider 210 may include vulnerability testingsystems, and secure email gateways can all produce information about thethreats that an organization or an individual has been exposed to, whichmay not be apparent to the security awareness system. In someembodiments, a user may use systems or software that have beendetermined to be vulnerable by a vulnerability testing system. In someembodiments, a user may have received a number of attacks which havebeen stopped by a secure email gateway before they reached the company'semail system. In some embodiments, a user may be recognized within aSIEM to be associated with one or multiple security threats or incidentsthat are recorded by the SIEM.

System 200 may include artificial intelligence machine learning system215. Artificial intelligence machine learning system 215 may containfrequency calculator 201, propensity calculator 202 and severitycalculator 203. In some embodiments, artificial intelligence machinelearning system 215 includes models that are configured to predict userresponses to malicious attacks based on data of the three calculators inorder to create an individual risk score. In some embodiments, data ofthe three calculators is integrated in order to create an individualrisk score. In some embodiments, data collection is performed on anongoing basis, and updated data and/or data sets may be used to re-trainmachine learning models or create new machine learning models thatevolve as the data changes. Artificial intelligence machine learningsystem 215 may receive data from simulated phishing campaign manager230, learning management manager 250, user group management manager 220,security services provider 210, or other external sources. Artificialintelligence machine learning system 215 may be part of server 106, maybe hosted separately on a different server, or on cloud 108. In anotherembodiment, artificial intelligence machine learning system 215 maycalculate aggregate group risk scores derived from one or moreindividual risk scores.

System 200 may include network 104. Network 104 may be any type and/orform of network. The geographical scope of network 104 may vary widelyand network 104 can be a body area network (BAN), a personal areanetwork (PAN), a local-area network (LAN), e.g. Intranet, a metropolitanarea network (MAN), a wide area network (WAN), or the Internet. Thetopology of network 104 may be of any form and may include, e.g., any ofthe following: point-to-point, bus, star, ring, mesh, or tree. Network104 may be an overlay network which is virtual and sits on top of one ormore layers of other networks 104. Network 104 may be of any suchnetwork topology as known to those ordinarily skilled in the art capableof supporting the operations described herein. Network 104 may utilizedifferent techniques and layers or stacks of protocols, including, e.g.,the Ethernet protocol, the internet protocol suite (TCP/IP), the ATM(Asynchronous Transfer Mode) technique, the SONET (Synchronous OpticalNetworking) protocol, or the SDH (Synchronous Digital Hierarchy)protocol. The TCP/IP internet protocol suite may include applicationlayer, transport layer, internet layer (including, e.g., IPv6), or thelink layer. Network 104 may be a type of a broadcast network, atelecommunications network, a data communication network, or a computernetwork.

Referring to FIG. 3, in a general overview, FIG. 3 depicts a method forestablishing a risk score. In step 302, artificial intelligence machinelearning system 215 determines a frequency score to predict a frequencyat which a user is to be hit with a malicious attack. In step 304,artificial intelligence machine learning system 215 determines apropensity score that identifies a propensity of the user to respond tothe hit of the malicious attack. In step 306, artificial intelligencemachine learning system 215 determines a severity score that identifiesa severity of the user's response to the hit of the malicious attack. Instep 308, artificial intelligence machine learning system 215establishes a risk score of the user based at least on the frequencyscore, the severity score and the propensity score. In step 310, basedon the risk score, artificial intelligence machine learning system 215displays a probability that the user will respond to a subsequent hit ofa type of malicious attack at a point in time.

Referring to FIG. 3 in more detail, In step 302, artificial intelligencemachine learning system 215 determines a frequency score to predict afrequency at which a user is to be hit with a malicious attack. In someembodiments, artificial intelligence machine learning system 215determines a frequency score based on a job score. The job score maycomprise a value based on a type of job. In examples, artificialintelligence machine learning system 215 determines a frequency scorebased on a breach score. The breach score the may comprise a valueidentified based on the user's level of exposure to email.

In embodiments, the risk score of an individual may be represented as:Risk Score (RS)=f{f(H), p(R|H), s(R,H)}=RS(f,p,s)where:H=a hit, de fined as any kind of malicious attackf(H)=frequency of potential harmful hits Hp(R|H)=the propensity that an individual will respond to a hit HR=individual response, e.g. a click, reply, etc.s(R,H)=the severity of the impact of response R to hit Hsubject to constraints:RS(0,0,0)≥0RS(1,1,1)=100In some embodiments, RS(f, p, s) is an increasing function of each ofits variables: f, p, and s.

In some embodiments, responses can include a multitude of user actions,for example but not limited to a user opening a message, clicking on alink in a message, replying to a message, opening an attachment to amessage, enabling a macro in a message, entering data in response to amessage, reporting a message, or forwarding a message. In some examples,data about responses may be aggregated, or temporal information may beincluded, for example the number of days since a user last responded, orhow many instances of each type of response (or any type of response) inthe last e.g. 30 days, 3 months, 1 year, etc.

In some embodiments, the training and learning history of a user may bepartitioned based on the type of course or module that the userperformed. In some examples, the training history may be divided intoshort modules (less than a predetermined duration) or long modules(greater than a predetermined duration). In some examples, traininghistory may be divided based on the type of training, for exampleclassroom training or online training. Training history may includecourses that the user is enrolled in by the system, courses that theuser has chosen to enroll in voluntarily, courses that the user hasstarted, or courses that the user has completed. Training history mayinclude the time interval between completed training courses.

In some embodiments, user data may be arranged in a tabular format,whereby the rows of the table represent a phish instance for a userwhich may include a detailed representation of the user and theirphishing and training history at a given point of time. In someembodiments, when training a risk score model, the system usesinformation in a table to learn how the user responded to a specificattack given their history at the time of the attack.

A component of the risk score calculation is f(H), which reflects thefrequency at which individuals are hit with a malicious attack (H). Insome embodiments, this information is based on two proxies in the data,job score and breach score. In some embodiments, job score may bedefined as follows:Job Score (job title)=J=(0,5,6,10)where:0=does not match a category or no information available5=accounting or IT6=high level, e.g. manager, director, lead10=executive, e.g. C×OIt is understood that these example job classifications aredemonstrative, and any job classifications may be used, and more orfewer job scores may be enabled.

In some embodiments, a breach score may be based on an email exposurecheck (EEC) threat level, for example a breach score may be defined asfollows:Breach Score (EEC)=B=(0,3,10)where:0=the user has negligable email exposure3=the user has moderate email exposure10=the user has high email exposureIt is understood that these example breach scores are demonstrative, andany breach scores may be used, and more or fewer breach scores may beenabled.

In some embodiments, breach score information may be decayed over time.In some examples, the data supporting the breach score inputs may besparse, or in some examples the users may not have a job title whichfits into any of these categories, in which case f(H) may take on abaseline value.

In step 304, artificial intelligence machine learning system 215determines a propensity score that identifies a propensity of the userto respond to the hit of the malicious attack. In embodiments, thepropensity score is based at least on training a predictive model withan input of the user history of whether or not the user responded with atype of response for a given hit of the malicious attack.

In some embodiments, the propensity p(R|H) represents the predictivemodel component of the risk score RS. In some examples, this value maybe produced by:

a. Training statistical and neural network models to learn the mappingfrom a particular user history (input features) to whether or not theuser responded (response R) on a given malicious attack (H).

b. After repeated exposure to millions of samples, the models are ableto learn to predict with some success whether or not users with givenmalicious attack and training histories are likely to perform a responseR given an attack H.

c. Given enough training data, models are able to predict a variety ofdifferent user responses R to a variety of different attacks H.

In some embodiments, the model's output, p(R|H) is the probability thata given user will respond, for example click, in response to a maliciousattack, for example a phishing email, at a particular point in time. Thesame user may exhibit a different p at a later time. To evaluate thepredictive performance of the model, we need more than one maliciousattack, which allows the model to predict the click rate of a user overa period of time and compare it to the user's actual click rate overthat period of time. For example, assume the period of time is one year.For each user, calculate the predicted p(R|H) each time a phishing emailhits over one year and record whether or not the user clicked. p(R|H) isharder to predict and more variable for users with few phish events. Themore phish data that is available about a user, the more accurate therisk score RS becomes. In some embodiments, the performance of the modelis assessed by predicting the number of clicks instead of the percentageover a period of time and comparing it to the user's actual clicks overthat period of time.

The value of p(R|H) is a very good predictor in the aggregate for agroup of users with similar profiles. When users are categorized bytheir predicted p(R|H) value, the percent of actual clicks in eachcategory closely tracks the predicted p(R|H) value for the category.Uncertainties at the individual level get smaller when groups ofindividuals are aggregated. The propensity component p(R|H) of the riskscore RS becomes more predictive as the number of phish eventsincreases. In some embodiments, this can be achieved by taking multipleevents into account for each user. In embodiments, this can be achievedby aggregating the expected click rate at the level of a group. In step306, artificial intelligence machine learning system 215 determines aseverity score that identifies a severity of the user's response to thehit of the malicious attack. In examples, the severity score may bebased at least on the job score. In some examples, the severity scoremay be based at least on a user's individual access.

In some embodiments, user training histories are used in predictingp(R|H) values. When more users have had training, p(R|H) is low, andwhen fewer users have had training, p(R|H) is high. In some examples,when users have had training recently, p(R|H) is low.

In some examples, calculation is the severity s(R, H) reflects theseverity of the user response R to malicious attack or hit H. In someembodiments s(R, H) may be a function of individual access. In someembodiments, the severity may be a function of the user's job score. Insome embodiments, the severity may by a function of a risk boostervalue, which may be set by a company or system administrator tocustomize the assessed risk of individuals or of groups of individuals.In some embodiments, severity score may be defined as follows:s(R,H)=(−1,0,1,10)where:−1=below normal risk0=normal risk (default)1=elevated risk10=very high risk

It is understood that these example severity score classifications aredemonstrative, and any severity score classifications may be used, andmore or fewer severity scores may be enabled.

In step 308, artificial intelligence machine learning system 215establishes a risk score for the user based at least on the frequencyscore, the severity score and the propensity score. In embodiments,artificial intelligence machine learning system 215 establishes a riskscore model as a function of the frequency score the propensity score,and the severity score. In examples, the function of the frequency scorethe propensity score, and the severity score may utilize a weighted orlogarithmic function.

In some embodiments, several different processes or workflows areinvolved in the prediction of risk score. In some embodiments, data aredivided up into groups, and a percentage of the data is used fortraining, a percentage of the data is used for validation, and apercentage of the data is used for text. In one examples, 80% of thedata is used for training, 10% of the data is used for validation, and10% of the data is used for test. In some embodiments, data fromarchived users may be used for any of training, validation or testing ofthe model. In some embodiments, the model is updated, trained orretrained periodically as new data enters the system. In someembodiments, this may be updated daily, weekly, monthly, or yearly. Insome embodiments, users may be added to or removed from groups that areused to train the models. For example, a user may more from one joblevel to a different job level, and all users of a given job level arebeing used to train a model for use with that job level. In thisexample, the user that moved job level would be removed from the groupof users used to train, validate or test the model for that job level.

The components of RS—f, p, and s, represent the expected loss due tomalicious attacks over a period of time. The function RS(f, p, s) insome embodiments may be represented as a weighted sum of logarithms:RS(f,p,s)=w ₁ log f+w ₂ log p+w ₃ log s

In some embodiments, the function RS(f, p, s) may be represented asfollows:

${{RS}\left( {f,p,s} \right)} = {{w_{1}{\log\left( {1 + J + B} \right)}} + {w_{2}{\log\left( {1 + {P\left( R \middle| H \right)}} \right)}} + \frac{w_{3}{\log\left( {\left( {1 + J} \right)\left( {1.5 + \frac{RB}{2}} \right)} \right)}}{d}}$where:w_(i)=settable parameters, normalized so that 0≤RS≤100J=job score (0, 5, 6, or 10)B=breach score (0, 3, or 10)RB=severity (s)(−1,0,1,10)and:

$d = {\frac{\log\left( {21 + {100{\log(2)}} + {\log\left( {11 \times 6.5} \right)}} \right)}{100} = {{0.77\mspace{14mu}{when}\mspace{14mu} w_{i}} = 1}}$

In embodiments, artificial intelligence machine learning system 215 mayestablish a group risk score based on a function of risk scores of eachuser within the group. The use of the highest individual risk score in agroup at the risk score for the group is not recommended as it puts toomuch weight on one single estimate. The average over all individual riskscores in the group is one possible approach to aggregating the riskscores, however this may tend to underestimate the contribution ofisolated outliers. In some examples, the approach taken may be referredto as using the standard distance to the perfect score (0), described asfollows:

${{RS}({group})} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {{RS}(i)}^{2} \right)}}$where:

N=number of group members

RS(i)=individual risk score of member i

As an example, consider the case where N=100, RS(1)=100, and RS(i)=0,i=2: 100. Then, RS_(max)=100, RS_(average)=1, and RS_(group)=10.

In step 310, based on the risk score, artificial intelligence machinelearning system 215 displays a probability that the user will respond toa subsequent hit of a type of malicious attack at a point in time.

Referring to FIG. 4, in general overview, FIG. 4 illustrates thepredictive performance of the model to calculate p(click|H) or a user'srisk score. The model's output p(click|H) is the probability that agiven user will click in response to a phishing email at a particularpoint in time. In some embodiments, the same user may exhibit adifferent p at a later point in time. In FIG. 4, the click rate of auser over a period of time is calculated and compared to the user'sactual click rate over that period of time to evaluate the predictiveperformance of the model. The greater the number of phish data samplesper user, the more accurately the risk score predicts the user's actualbehavior.

It should be understood that the systems described above may providemultiple ones of any or each of those components and these componentsmay be provided on either a standalone machine or, in some embodiments,on multiple machines in a distributed system. The systems and methodsdescribed above may be implemented as a method, apparatus or article ofmanufacture using programming and/or engineering techniques to producesoftware, firmware, hardware, or any combination thereof. In addition,the systems and methods described above may be provided as one or morecomputer-readable programs embodied on or in one or more articles ofmanufacture. The term “article of manufacture” as used herein isintended to encompass code or logic accessible from and embedded in oneor more computer-readable devices, firmware, programmable logic, memorydevices (e.g., EEPROMs, ROMs, PROMS, RAMS, SRAMs, etc.), hardware (e.g.,integrated circuit chip, Field Programmable Gate Array (FPGA),Application Specific Integrated Circuit (ASIC), etc.), electronicdevices, a computer readable non-volatile storage unit (e.g., CD-ROM,floppy disk, hard disk drive, etc.). The article of manufacture may beaccessible from a file server providing access to the computer-readableprograms via a network transmission line, wireless transmission media,signals propagating through space, radio waves, infrared signals, etc.The article of manufacture may be a flash memory card or a magnetictape. The article of manufacture includes hardware logic as well assoftware or programmable code embedded in a computer readable mediumthat is executed by a processor. In general, the computer-readableprograms may be implemented in any programming language, such as LISP,PERL, C, C++, C#, PROLOG, or in any byte code language such as JAVA. Thesoftware programs may be stored on or in one or more articles ofmanufacture as object code.

While various embodiments of the methods and systems have beendescribed, these embodiments are illustrative and in no way limit thescope of the described methods or systems. Those having skill in therelevant art can effect changes to form and details of the describedmethods and systems without departing from the broadest scope of thedescribed methods and systems. Thus, the scope of the methods andsystems described herein should not be limited by any of theillustrative embodiments and should be defined in accordance with theaccompanying claims and their equivalents.

We claim:
 1. A method comprising: (a) determining, by one or more servers, a frequency score to predict a frequency at which a user is to be hit with a malicious attack; (b) determining, by the one or more servers, a propensity score that identifies a propensity of the user to respond to the hit of the malicious attack; (c) determining, by the one or more servers, a severity score that identifies a severity of the user's response to the hit of the malicious attack; (d) establishing, by the one or more servers, a risk score for the user, the risk score established as a function of the frequency score, the severity score and the propensity score, wherein the function comprises one of a weighted or logarithmic function; and (e) displaying, by the one or more servers based at least on the risk score, a probability that the user will respond to a subsequent hit of a type of malicious attack at a point in time.
 2. The method of claim 1, wherein (a) further comprise determining the frequency score based at least on a job score and a breach score.
 3. The method of claim 2, wherein the job score comprises a value identified based on a type of job.
 4. The method of claim 2, wherein the breach score comprises a value identified based on the user's level of exposure to email.
 5. The method of claim 1, wherein (b) further comprises determining the propensity score based at least on training a predictive model with an input of the user history of whether or not the user responded with a type of response for a given hit of the malicious attack.
 6. The method of claim 1, wherein (c) further comprises determining the severity score based at least on a job score.
 7. The method of claim 1, wherein (c) further comprises determining the severity score based at least on individual access of the user.
 8. The method of claim 1, further comprising establishing a group risk score based on a function of risk scores of each user within the group.
 9. A system comprising: one or more servers comprising one or more processors and configured to: determine a frequency score to predict a frequency at which a user is to be hit with a malicious attack; determine a propensity score that identifies a propensity of the user to respond to the hit of the malicious attack; determine a severity score that identifies a severity of the user's response to the hit of the malicious attack; establish a risk score for the user, the risk score established as a function of the frequency score, the severity score and the propensity score, wherein the function comprises one of a weighted or logarithmic function; and display, based at least on the risk score, a probability that the user will respond to a subsequent hit of a type of malicious attack at a point in time.
 10. The system of claim 9, wherein the one or more servers are further configured to determine the frequency score based at least on a job score and a breach score.
 11. The system of claim 10, wherein the job score comprises a value identified based on a type of job.
 12. The system of claim 10, wherein the breach score comprises a value identified based on the user's level of exposure to email.
 13. The system of claim 9, wherein the one or more servers are further configured to determine the propensity score based at least on training a predictive model with an input of the user history of whether or not the user responded with a type of response for a given hit of the malicious attack.
 14. The system of claim 9, wherein the one or more servers are further configured to determine the severity score based at least on a job score.
 15. The system of claim 9, wherein the one or more servers are further configured to determine the severity score based at least on individual access of the user.
 16. The system of claim 9, wherein the one or more servers are further configured to establish a group risk score based on a function of risk scores of each user within the group. 